Cardiovascular diseases (CVDs) are debilitating illnesses that afflict millions of people in the world each year. Indeed, in 1997, over 450,000 people in the U.S. alone died from myocardial infarctions; one of every five deaths in that calendar year. In addition to myocardial infarction, cardiovascular diseases result in hypertension, angina, arteriosclerosis, and atherosclerosis. Angina, for example, accounts for more than 1 million hospital admissions annually in the U.S., and 6-8 percent of subjects with this condition either have non-fatal myocardial infarction, or die, within the first year after diagnosis.
Cardiovascular diseases are also a major cause of morbidity and mortality in subsets of the population already suffering from other disorders. For example, cardiovascular disease are the major cause of mortality in end-stage renal disease (ESRD) subjects (1). Coronary artery disease (CAD) is reported to occur in 40-60% of incident hemodialysis subjects and this figure rises to more than 50% for diabetic hemodialysis subjects (1-3). The annual incidence of new coronary artery disease in dialysis subjects is 30-40 times higher than in the general population (4). The rate at which hemodialysis subjects in the United States are hospitalized for their first acute coronary event (myocardial infarction or unstable angina) is 2.9-3.3 per 100 subject-years (5) and the annual overall mortality and cardiac mortality following acute myocardial infarction were 62% and 42%, respectively, between 1990 and 1995 (6). Moreover, hemodialysis subjects are significantly less likely to be evaluated with cardiac catheterization and therefore have a lower incidence of coronary revascularization procedures (7).
Hence, the ability to accurately identify subjects with CVDs in the general population, as well as in subpopulations identified as at high risk for CVDs, such as the ESRD population, is of great importance.
Currently, physicians are able to diagnose CVD in subjects who have already begun to experience symptoms. For example, the levels of certain cardiac-associated enzymes, such as creatine kinase, are elevated after myocardial infarction, and may be detected an enzyme-specific assay. Likewise, coronary angiography is the definitive means of diagnosing CAD. However, it is an expensive and invasive procedure. Algorithms have been developed for non-invasively assessing the risk of CAD in the general population. These algorithms are based on well-established risk factors, including dyslipidemia, smoking, hypertension and diabetes. While studies show that at least one of these risk factors is present in 80-90% of subjects with coronary artery disease in the general population, it has been estimated that they explain only about 75% of the occurrence of CAD. Further, traditional risk factors for CAD derived from studies of the normal population have limited applicability in at risk subpopulations, such as for example hemodialysis subjects. The prevalence of dyslipidemia, hypertension, diabetes and left ventricular hypertrophy is higher in hemodialysis subjects than in the general population and the relationship between some of these traditional risk factors and cardiovascular outcomes appears to be different in hemodialysis subjects than it is in the normal population. For example, the relationship of both hypertension and cholesterol to coronary heart disease and mortality is U-shaped in hemodialysis subjects, with higher blood pressures and cholesterol concentrations conferring a survival advantage (8, 9).
CVD constitutes a considerable medical and economic burden. Current diagnostic approaches are either invasive, expensive, associated with the risk of complications, or their interpretation is confounded by concurrent medical conditions. Alternative, non-invasive approaches are needed that address these limitations. Thus, there is currently an unmet need for new, more specific biomarkers of CVD that can be used to identify subjects suffering from (even if physiologically asymptomatic) or at risk of CVD for targeted interventions.